The general aim of this project is to develop new and flexible statistical methods for the detection of clustering within spatial cancer disease incidence data. In this connection we are interested in developing and evaluating the use of cluster modeling methods to address the issue of the identification of where clusters are and how large the clusters are. Current accepted methodology in this area is limited by its basis on hypothesis testing (e.g. SaTScan [1]), and there is considerable scope to develop new model-based methods for this purpose. The major specific aims are: 1) The development of Bayesian models that allow dependence on the local density of cases (data-dependent models). 2) The development of methods in not only spatial settings but also within space-time situations. This is important in public health applications where changes in time can have particular importance. We propose to extend the data-dependent spatial cluster modeling to the temporal setting by using a hierarchical Bayesian approach to modeling the region and year specific cluster distributions, in particular, to estimate the excess of disease risk within a space-time continuum. 3) The evaluation of the developed methods. We propose to evaluate the use of data-dependent cluster models in comparison to a small set of alternative comparison methods. 4) The development of software. There is a need for flexible software to be made available that can allow researchers and public health workers tasked with cancer cluster detection to be able to use modeling approaches, with their ability to flexibly build appropriate descriptions of the observed disease data.